Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations4877540
Missing cells5010280
Missing cells (%)4.9%
Duplicate rows17
Duplicate rows (%)< 0.1%
Total size in memory781.5 MiB
Average record size in memory168.0 B

Variable types

Text2
Numeric12
Categorical3
DateTime2
Unsupported2

Alerts

Dataset has 17 (< 0.1%) duplicate rowsDuplicates
cd_centro_custo is highly overall correlated with ds_estabelecimentoHigh correlation
cd_local_estoque is highly overall correlated with ds_estabelecimentoHigh correlation
cd_material is highly overall correlated with id_itemHigh correlation
cd_operacao is highly overall correlated with ds_operacao and 1 other fieldsHigh correlation
ds_estabelecimento is highly overall correlated with cd_centro_custo and 1 other fieldsHigh correlation
ds_operacao is highly overall correlated with cd_operacaoHigh correlation
id_item is highly overall correlated with cd_materialHigh correlation
qt_consumo is highly overall correlated with cd_operacao and 1 other fieldsHigh correlation
qt_estoque is highly overall correlated with qt_consumoHigh correlation
vl_consumo is highly overall correlated with vl_estoque and 2 other fieldsHigh correlation
vl_estoque is highly overall correlated with vl_consumo and 2 other fieldsHigh correlation
vl_movimento is highly overall correlated with vl_consumo and 2 other fieldsHigh correlation
vl_movimento_2 is highly overall correlated with vl_consumo and 2 other fieldsHigh correlation
ds_estabelecimento is highly imbalanced (94.3%)Imbalance
cd_acao is highly imbalanced (99.3%)Imbalance
ds_operacao is highly imbalanced (85.9%)Imbalance
nr_atendimento has 132655 (2.7%) missing valuesMissing
nr_lote_contabil has 4877540 (100.0%) missing valuesMissing
cd_centro_custo is highly skewed (γ1 = -25.00265505)Skewed
cd_operacao is highly skewed (γ1 = 25.00912097)Skewed
qt_estoque is highly skewed (γ1 = 2190.824078)Skewed
vl_estoque is highly skewed (γ1 = 271.0042698)Skewed
vl_movimento is highly skewed (γ1 = 271.0042698)Skewed
vl_consumo is highly skewed (γ1 = 271.8533025)Skewed
qt_consumo is highly skewed (γ1 = 2191.238896)Skewed
vl_movimento_2 is highly skewed (γ1 = 271.8533025)Skewed
cd_conta_contabil is an unsupported type, check if it needs cleaning or further analysisUnsupported
nr_lote_contabil is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-09-26 21:04:17.649618
Analysis finished2025-09-26 21:09:37.779827
Duration5 minutes and 20.13 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct220
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.2 MiB
2025-09-26T18:09:38.062746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length54
Median length50
Mean length24.281995
Min length3

Characters and Unicode

Total characters118436403
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)< 0.1%

Sample

1st rowPosto Carambeí
2nd rowSADT - RADIOLOGIA
3rd rowColeta Ambulatorial
4th row3º ANDAR - UNIDADE DE INTERNAÇÃO
5th row3º ANDAR - UNIDADE DE INTERNAÇÃO
ValueCountFrequency (%)
de2224137
 
11.2%
unidade2116502
 
10.6%
internação2109717
 
10.6%
1814792
 
9.1%
andar1744095
 
8.7%
centro1065732
 
5.3%
cirúrgico963438
 
4.8%
unimed837602
 
4.2%
24678581
 
3.4%
horas678581
 
3.4%
Other values (271)5708350
28.6%
2025-09-26T18:09:38.708378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15093392
12.7%
A9672749
 
8.2%
N9660202
 
8.2%
I9590087
 
8.1%
D8894184
 
7.5%
R8801372
 
7.4%
E8708351
 
7.4%
O6592818
 
5.6%
T5563631
 
4.7%
U4400999
 
3.7%
Other values (66)31458618
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)118436403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15093392
12.7%
A9672749
 
8.2%
N9660202
 
8.2%
I9590087
 
8.1%
D8894184
 
7.5%
R8801372
 
7.4%
E8708351
 
7.4%
O6592818
 
5.6%
T5563631
 
4.7%
U4400999
 
3.7%
Other values (66)31458618
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)118436403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15093392
12.7%
A9672749
 
8.2%
N9660202
 
8.2%
I9590087
 
8.1%
D8894184
 
7.5%
R8801372
 
7.4%
E8708351
 
7.4%
O6592818
 
5.6%
T5563631
 
4.7%
U4400999
 
3.7%
Other values (66)31458618
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)118436403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15093392
12.7%
A9672749
 
8.2%
N9660202
 
8.2%
I9590087
 
8.1%
D8894184
 
7.5%
R8801372
 
7.4%
E8708351
 
7.4%
O6592818
 
5.6%
T5563631
 
4.7%
U4400999
 
3.7%
Other values (66)31458618
26.6%

cd_centro_custo
Real number (ℝ)

High correlation  Skewed 

Distinct224
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9104.8642
Minimum101
Maximum19334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 MiB
2025-09-26T18:09:38.795362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile9101
Q19107
median9108
Q39124
95-th percentile9143
Maximum19334
Range19233
Interquartile range (IQR)17

Descriptive statistics

Standard deviation288.21908
Coefficient of variation (CV)0.031655506
Kurtosis711.0801
Mean9104.8642
Median Absolute Deviation (MAD)5
Skewness-25.002655
Sum4.440934 × 1010
Variance83070.24
MonotonicityNot monotonic
2025-09-26T18:09:38.926378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9108962092
19.7%
9109677102
13.9%
9107505888
10.4%
9103453493
9.3%
9101439363
9.0%
9143384940
7.9%
9124365621
 
7.5%
9102272906
 
5.6%
9142193391
 
4.0%
9137185906
 
3.8%
Other values (214)436838
9.0%
ValueCountFrequency (%)
101129
< 0.1%
10555
< 0.1%
10716
 
< 0.1%
10860
< 0.1%
11050
 
< 0.1%
11192
< 0.1%
1121
 
< 0.1%
11315
 
< 0.1%
20921
 
< 0.1%
21417
 
< 0.1%
ValueCountFrequency (%)
193346
 
< 0.1%
193071
 
< 0.1%
1930611
 
< 0.1%
193041
 
< 0.1%
191351
 
< 0.1%
191146
 
< 0.1%
191021
 
< 0.1%
114041
 
< 0.1%
98111
 
< 0.1%
9801297
< 0.1%

ds_estabelecimento
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.2 MiB
Hospital Geral Unimed
4807884 
Laboratorio Unimed Ponta Grossa
 
31480
ESU Telêmaco Borba - Klabin
 
24151
Espaço Saúde Unimed
 
9859
Unimed Ponta Grossa
 
4166

Length

Max length31
Median length21
Mean length21.088499
Min length19

Characters and Unicode

Total characters102859996
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaboratorio Unimed Ponta Grossa
2nd rowHospital Geral Unimed
3rd rowLaboratorio Unimed Ponta Grossa
4th rowHospital Geral Unimed
5th rowHospital Geral Unimed

Common Values

ValueCountFrequency (%)
Hospital Geral Unimed4807884
98.6%
Laboratorio Unimed Ponta Grossa31480
 
0.6%
ESU Telêmaco Borba - Klabin24151
 
0.5%
Espaço Saúde Unimed9859
 
0.2%
Unimed Ponta Grossa4166
 
0.1%

Length

2025-09-26T18:09:39.025975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-26T18:09:39.092667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
unimed4853389
33.0%
hospital4807884
32.7%
geral4807884
32.7%
ponta35646
 
0.2%
grossa35646
 
0.2%
laboratorio31480
 
0.2%
esu24151
 
0.2%
telêmaco24151
 
0.2%
borba24151
 
0.2%
24151
 
0.2%
Other values (3)43869
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a9842191
 
9.6%
9834862
 
9.6%
i9716904
 
9.4%
e9695283
 
9.4%
l9664070
 
9.4%
o5031777
 
4.9%
r4930641
 
4.8%
n4913186
 
4.8%
s4889035
 
4.8%
m4877540
 
4.7%
Other values (19)29464507
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)102859996
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a9842191
 
9.6%
9834862
 
9.6%
i9716904
 
9.4%
e9695283
 
9.4%
l9664070
 
9.4%
o5031777
 
4.9%
r4930641
 
4.8%
n4913186
 
4.8%
s4889035
 
4.8%
m4877540
 
4.7%
Other values (19)29464507
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)102859996
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a9842191
 
9.6%
9834862
 
9.6%
i9716904
 
9.4%
e9695283
 
9.4%
l9664070
 
9.4%
o5031777
 
4.9%
r4930641
 
4.8%
n4913186
 
4.8%
s4889035
 
4.8%
m4877540
 
4.7%
Other values (19)29464507
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)102859996
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a9842191
 
9.6%
9834862
 
9.6%
i9716904
 
9.4%
e9695283
 
9.4%
l9664070
 
9.4%
o5031777
 
4.9%
r4930641
 
4.8%
n4913186
 
4.8%
s4889035
 
4.8%
m4877540
 
4.7%
Other values (19)29464507
28.6%

cd_material
Real number (ℝ)

High correlation 

Distinct3637
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25426.129
Minimum2
Maximum79674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 MiB
2025-09-26T18:09:39.167923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile69
Q1238
median4242
Q352139
95-th percentile73864
Maximum79674
Range79672
Interquartile range (IQR)51901

Descriptive statistics

Standard deviation28179.3
Coefficient of variation (CV)1.1082812
Kurtosis-1.5141952
Mean25426.129
Median Absolute Deviation (MAD)4174
Skewness0.46251779
Sum1.2401696 × 1011
Variance7.9407292 × 108
MonotonicityNot monotonic
2025-09-26T18:09:39.258987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51823540978
 
11.1%
69328797
 
6.7%
2358286057
 
5.9%
68167287
 
3.4%
53709156174
 
3.2%
342153863
 
3.2%
90140266
 
2.9%
11325116066
 
2.4%
193107909
 
2.2%
7386492689
 
1.9%
Other values (3627)2787454
57.1%
ValueCountFrequency (%)
260
 
< 0.1%
321258
 
< 0.1%
351569
 
< 0.1%
361298
 
< 0.1%
37181
 
< 0.1%
381503
 
< 0.1%
407492
 
0.2%
556775
 
0.1%
6116
 
< 0.1%
68167287
3.4%
ValueCountFrequency (%)
796741
 
< 0.1%
796291
 
< 0.1%
796281
 
< 0.1%
795941
 
< 0.1%
795741
 
< 0.1%
79548156
< 0.1%
7954724
 
< 0.1%
7954666
< 0.1%
7954563
< 0.1%
795342
 
< 0.1%

cd_acao
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.2 MiB
1
4874868 
2
 
2672

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4877540
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
14874868
99.9%
22672
 
0.1%

Length

2025-09-26T18:09:39.368592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-26T18:09:39.486187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
14874868
99.9%
22672
 
0.1%

Most occurring characters

ValueCountFrequency (%)
14874868
99.9%
22672
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4877540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14874868
99.9%
22672
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4877540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14874868
99.9%
22672
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4877540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14874868
99.9%
22672
 
0.1%

nr_atendimento
Real number (ℝ)

Missing 

Distinct134087
Distinct (%)2.8%
Missing132655
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean2275040.8
Minimum2140
Maximum2635382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 MiB
2025-09-26T18:09:39.612608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2140
5-th percentile1940154
Q12063674
median2297411
Q32475582
95-th percentile2604612
Maximum2635382
Range2633242
Interquartile range (IQR)411908

Descriptive statistics

Standard deviation224912.51
Coefficient of variation (CV)0.098860871
Kurtosis-0.95951814
Mean2275040.8
Median Absolute Deviation (MAD)207437
Skewness-0.16531884
Sum1.0794807 × 1013
Variance5.0585638 × 1010
MonotonicityNot monotonic
2025-09-26T18:09:39.726250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225573014999
 
0.3%
231403214414
 
0.3%
149864812830
 
0.3%
21568548873
 
0.2%
25642698678
 
0.2%
24755827524
 
0.2%
19762327337
 
0.2%
23393567318
 
0.2%
25166297265
 
0.1%
19400587071
 
0.1%
Other values (134077)4648576
95.3%
(Missing)132655
 
2.7%
ValueCountFrequency (%)
21402
 
< 0.1%
2352151
 
< 0.1%
2526462
 
< 0.1%
29636511
 
< 0.1%
4598421
 
< 0.1%
10565305
 
< 0.1%
1137099188
 
< 0.1%
1284595634
 
< 0.1%
149864812830
0.3%
1565052196
 
< 0.1%
ValueCountFrequency (%)
263538211
 
< 0.1%
26353814
 
< 0.1%
263538012
 
< 0.1%
26353795
 
< 0.1%
263537611
 
< 0.1%
263537312
 
< 0.1%
263536824
< 0.1%
263536340
< 0.1%
263536155
< 0.1%
263536017
 
< 0.1%
Distinct1522221
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size37.2 MiB
Minimum2011-07-22 10:00:00
Maximum2024-12-31 23:53:10
Invalid dates0
Invalid dates (%)0.0%
2025-09-26T18:09:39.845574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:39.942977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cd_local_estoque
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.321213
Minimum1
Maximum565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 MiB
2025-09-26T18:09:40.067615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q312
95-th percentile57
Maximum565
Range564
Interquartile range (IQR)11

Descriptive statistics

Standard deviation81.374238
Coefficient of variation (CV)4.0043987
Kurtosis34.170705
Mean20.321213
Median Absolute Deviation (MAD)0
Skewness5.9574757
Sum99117527
Variance6621.7665
MonotonicityNot monotonic
2025-09-26T18:09:40.163249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12797386
57.4%
121002612
 
20.6%
15756741
 
15.5%
57129146
 
2.6%
50649799
 
1.0%
1146986
 
1.0%
55123356
 
0.5%
6217734
 
0.4%
51515514
 
0.3%
5617968
 
0.2%
Other values (51)30298
 
0.6%
ValueCountFrequency (%)
12797386
57.4%
1146986
 
1.0%
121002612
 
20.6%
15756741
 
15.5%
17131
 
< 0.1%
18105
 
< 0.1%
1917
 
< 0.1%
2143
 
< 0.1%
2319
 
< 0.1%
24181
 
< 0.1%
ValueCountFrequency (%)
5652
 
< 0.1%
56413
 
< 0.1%
56318
 
< 0.1%
5617968
 
0.2%
5601876
 
< 0.1%
554795
 
< 0.1%
55123356
0.5%
5506
 
< 0.1%
5481
 
< 0.1%
5441323
 
< 0.1%
Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.2 MiB
2025-09-26T18:09:40.286334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length8
Mean length14.344143
Min length3

Characters and Unicode

Total characters69964129
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowPosto Carambeí
2nd rowAlmoxarifado
3rd rowAlmoxarifado Laboratório
4th rowAlmoxarifado
5th rowAlmoxarifado
ValueCountFrequency (%)
farmacia3807982
39.9%
centro1053715
 
11.0%
cirurgico1002612
 
10.5%
farmácia780097
 
8.2%
unimed756741
 
7.9%
24756741
 
7.9%
horas756741
 
7.9%
caf131285
 
1.4%
90977
 
1.0%
almoxarifado69681
 
0.7%
Other values (76)331172
 
3.5%
2025-09-26T18:09:40.463835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a13966229
20.0%
r8516788
12.2%
i7530444
10.8%
c5625581
8.0%
m5405483
 
7.7%
F4719382
 
6.7%
4663030
 
6.7%
o3093326
 
4.4%
C2295457
 
3.3%
e1883994
 
2.7%
Other values (53)12264415
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)69964129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a13966229
20.0%
r8516788
12.2%
i7530444
10.8%
c5625581
8.0%
m5405483
 
7.7%
F4719382
 
6.7%
4663030
 
6.7%
o3093326
 
4.4%
C2295457
 
3.3%
e1883994
 
2.7%
Other values (53)12264415
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)69964129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a13966229
20.0%
r8516788
12.2%
i7530444
10.8%
c5625581
8.0%
m5405483
 
7.7%
F4719382
 
6.7%
4663030
 
6.7%
o3093326
 
4.4%
C2295457
 
3.3%
e1883994
 
2.7%
Other values (53)12264415
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)69964129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a13966229
20.0%
r8516788
12.2%
i7530444
10.8%
c5625581
8.0%
m5405483
 
7.7%
F4719382
 
6.7%
4663030
 
6.7%
o3093326
 
4.4%
C2295457
 
3.3%
e1883994
 
2.7%
Other values (53)12264415
17.5%

cd_conta_contabil
Unsupported

Rejected  Unsupported 

Missing85
Missing (%)< 0.1%
Memory size37.2 MiB

nr_lote_contabil
Unsupported

Missing  Rejected  Unsupported 

Missing4877540
Missing (%)100.0%
Memory size37.2 MiB

ds_operacao
Categorical

High correlation  Imbalance 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.2 MiB
Execução Prescrição
4468465 
Devolução Paciente
 
276155
Consumo
 
124888
Quebras e Contaminações
 
2404
Produtos vencidos
 
2353
Other values (7)
 
3275

Length

Max length36
Median length19
Mean length18.6399
Min length7

Characters and Unicode

Total characters90916856
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumo
2nd rowConsumo
3rd rowConsumo
4th rowConsumo
5th rowConsumo

Common Values

ValueCountFrequency (%)
Execução Prescrição4468465
91.6%
Devolução Paciente276155
 
5.7%
Consumo124888
 
2.6%
Quebras e Contaminações2404
 
< 0.1%
Produtos vencidos2353
 
< 0.1%
Perdas e Quebras1746
 
< 0.1%
Quebras/Contaminação Med Controlados881
 
< 0.1%
Perdas por estabilidade238
 
< 0.1%
Sobras por estabilidade222
 
< 0.1%
Medicamentos Controlados Vencidos125
 
< 0.1%
Other values (2)63
 
< 0.1%

Length

2025-09-26T18:09:40.517002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
execução4468465
46.4%
prescrição4468465
46.4%
devolução276155
 
2.9%
paciente276155
 
2.9%
consumo124888
 
1.3%
quebras4150
 
< 0.1%
e4150
 
< 0.1%
vencidos2478
 
< 0.1%
contaminações2404
 
< 0.1%
produtos2353
 
< 0.1%
Other values (13)6250
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e9783556
10.8%
o9754338
10.7%
ç9216391
10.1%
c9215814
10.1%
ã9213966
10.1%
r8948112
9.8%
u4876892
 
5.4%
4758373
 
5.2%
i4751470
 
5.2%
P4748957
 
5.2%
Other values (22)15648987
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)90916856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e9783556
10.8%
o9754338
10.7%
ç9216391
10.1%
c9215814
10.1%
ã9213966
10.1%
r8948112
9.8%
u4876892
 
5.4%
4758373
 
5.2%
i4751470
 
5.2%
P4748957
 
5.2%
Other values (22)15648987
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)90916856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e9783556
10.8%
o9754338
10.7%
ç9216391
10.1%
c9215814
10.1%
ã9213966
10.1%
r8948112
9.8%
u4876892
 
5.4%
4758373
 
5.2%
i4751470
 
5.2%
P4748957
 
5.2%
Other values (22)15648987
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)90916856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e9783556
10.8%
o9754338
10.7%
ç9216391
10.1%
c9215814
10.1%
ã9213966
10.1%
r8948112
9.8%
u4876892
 
5.4%
4758373
 
5.2%
i4751470
 
5.2%
P4748957
 
5.2%
Other values (22)15648987
17.2%

cd_operacao
Real number (ℝ)

High correlation  Skewed 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1372702
Minimum1
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 MiB
2025-09-26T18:09:40.626190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile5
Maximum141
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9696515
Coefficient of variation (CV)0.95948566
Kurtosis656.52644
Mean4.1372702
Median Absolute Deviation (MAD)0
Skewness25.009121
Sum20179701
Variance15.758133
MonotonicityNot monotonic
2025-09-26T18:09:40.692315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
44468465
91.6%
5276155
 
5.7%
1124888
 
2.6%
872404
 
< 0.1%
922353
 
< 0.1%
1211746
 
< 0.1%
91881
 
< 0.1%
137238
 
< 0.1%
136222
 
< 0.1%
93125
 
< 0.1%
Other values (2)63
 
< 0.1%
ValueCountFrequency (%)
1124888
 
2.6%
44468465
91.6%
5276155
 
5.7%
872404
 
< 0.1%
91881
 
< 0.1%
922353
 
< 0.1%
93125
 
< 0.1%
1211746
 
< 0.1%
13221
 
< 0.1%
136222
 
< 0.1%
ValueCountFrequency (%)
14142
 
< 0.1%
137238
 
< 0.1%
136222
 
< 0.1%
13221
 
< 0.1%
1211746
 
< 0.1%
93125
 
< 0.1%
922353
 
< 0.1%
91881
 
< 0.1%
872404
 
< 0.1%
5276155
5.7%
Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.2 MiB
Minimum2023-01-01 00:00:00
Maximum2024-12-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-26T18:09:40.759419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:40.900617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

qt_estoque
Real number (ℝ)

High correlation  Skewed 

Distinct1608
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0178746
Minimum-3600
Maximum990000
Zeros124
Zeros (%)< 0.1%
Negative2653
Negative (%)0.1%
Memory size37.2 MiB
2025-09-26T18:09:41.026096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3600
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum990000
Range993600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation449.46833
Coefficient of variation (CV)148.93539
Kurtosis4825458.8
Mean3.0178746
Median Absolute Deviation (MAD)0
Skewness2190.8241
Sum14719804
Variance202021.78
MonotonicityNot monotonic
2025-09-26T18:09:41.167555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14037789
82.8%
2458130
 
9.4%
396869
 
2.0%
462103
 
1.3%
548308
 
1.0%
616237
 
0.3%
1011836
 
0.2%
0.19020
 
0.2%
0.0838909
 
0.2%
205698
 
0.1%
Other values (1598)122641
 
2.5%
ValueCountFrequency (%)
-36001
 
< 0.1%
-25001
 
< 0.1%
-23501
 
< 0.1%
-20003
< 0.1%
-15001
 
< 0.1%
-14951
 
< 0.1%
-13001
 
< 0.1%
-1263.191
 
< 0.1%
-12501
 
< 0.1%
-1205.641
 
< 0.1%
ValueCountFrequency (%)
9900001
 
< 0.1%
92281
 
< 0.1%
60002
 
< 0.1%
47001
 
< 0.1%
44991
 
< 0.1%
44401
 
< 0.1%
42981
 
< 0.1%
42501
 
< 0.1%
400011
< 0.1%
38001
 
< 0.1%

vl_estoque
Real number (ℝ)

High correlation  Skewed 

Distinct24549
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.205163
Minimum-35475
Maximum376210.29
Zeros972
Zeros (%)< 0.1%
Negative2936
Negative (%)0.1%
Memory size37.2 MiB
2025-09-26T18:09:41.267423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-35475
5-th percentile0.2
Q10.42
median1.22
Q34.15
95-th percentile24.18
Maximum376210.29
Range411685.29
Interquartile range (IQR)3.73

Descriptive statistics

Standard deviation970.87769
Coefficient of variation (CV)43.723061
Kurtosis97058.845
Mean22.205163
Median Absolute Deviation (MAD)1.01
Skewness271.00427
Sum1.0830657 × 108
Variance942603.49
MonotonicityNot monotonic
2025-09-26T18:09:41.413103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2249510
 
5.1%
0.21157349
 
3.2%
0.53133199
 
2.7%
0.28105235
 
2.2%
1104299
 
2.1%
3.9396541
 
2.0%
0.585429
 
1.8%
0.3380540
 
1.7%
0.5277710
 
1.6%
0.8364205
 
1.3%
Other values (24539)3723523
76.3%
ValueCountFrequency (%)
-354751
< 0.1%
-18045.551
< 0.1%
-17223.411
< 0.1%
-14352.842
< 0.1%
-11772.131
< 0.1%
-111701
< 0.1%
-10222.941
< 0.1%
-9809.991
< 0.1%
-9253.341
< 0.1%
-9070.11
< 0.1%
ValueCountFrequency (%)
376210.298
< 0.1%
360009.8512
< 0.1%
340918.424
 
< 0.1%
176334.651
 
< 0.1%
153859.881
 
< 0.1%
1301202
 
< 0.1%
1107001
 
< 0.1%
101708.521
 
< 0.1%
99222.641
 
< 0.1%
94593.871
 
< 0.1%

vl_movimento
Real number (ℝ)

High correlation  Skewed 

Distinct24549
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.205163
Minimum-35475
Maximum376210.29
Zeros972
Zeros (%)< 0.1%
Negative2936
Negative (%)0.1%
Memory size37.2 MiB
2025-09-26T18:09:41.529105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-35475
5-th percentile0.2
Q10.42
median1.22
Q34.15
95-th percentile24.18
Maximum376210.29
Range411685.29
Interquartile range (IQR)3.73

Descriptive statistics

Standard deviation970.87769
Coefficient of variation (CV)43.723061
Kurtosis97058.845
Mean22.205163
Median Absolute Deviation (MAD)1.01
Skewness271.00427
Sum1.0830657 × 108
Variance942603.49
MonotonicityNot monotonic
2025-09-26T18:09:41.612851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2249510
 
5.1%
0.21157349
 
3.2%
0.53133199
 
2.7%
0.28105235
 
2.2%
1104299
 
2.1%
3.9396541
 
2.0%
0.585429
 
1.8%
0.3380540
 
1.7%
0.5277710
 
1.6%
0.8364205
 
1.3%
Other values (24539)3723523
76.3%
ValueCountFrequency (%)
-354751
< 0.1%
-18045.551
< 0.1%
-17223.411
< 0.1%
-14352.842
< 0.1%
-11772.131
< 0.1%
-111701
< 0.1%
-10222.941
< 0.1%
-9809.991
< 0.1%
-9253.341
< 0.1%
-9070.11
< 0.1%
ValueCountFrequency (%)
376210.298
< 0.1%
360009.8512
< 0.1%
340918.424
 
< 0.1%
176334.651
 
< 0.1%
153859.881
 
< 0.1%
1301202
 
< 0.1%
1107001
 
< 0.1%
101708.521
 
< 0.1%
99222.641
 
< 0.1%
94593.871
 
< 0.1%

vl_consumo
Real number (ℝ)

High correlation  Skewed 

Distinct27585
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.434551
Minimum-71357.13
Maximum376210.29
Zeros8894
Zeros (%)0.2%
Negative278733
Negative (%)5.7%
Memory size37.2 MiB
2025-09-26T18:09:41.792639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-71357.13
5-th percentile-0.24
Q10.34
median1.06
Q33.93
95-th percentile23.04
Maximum376210.29
Range447567.42
Interquartile range (IQR)3.59

Descriptive statistics

Standard deviation967.73663
Coefficient of variation (CV)47.357861
Kurtosis98195.714
Mean20.434551
Median Absolute Deviation (MAD)0.86
Skewness271.8533
Sum99670340
Variance936514.18
MonotonicityNot monotonic
2025-09-26T18:09:41.976066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2236099
 
4.8%
0.21148809
 
3.1%
0.53125250
 
2.6%
0.28101664
 
2.1%
199080
 
2.0%
3.9390394
 
1.9%
0.580524
 
1.7%
0.3375667
 
1.6%
0.5272703
 
1.5%
0.8360578
 
1.2%
Other values (27575)3786772
77.6%
ValueCountFrequency (%)
-71357.131
 
< 0.1%
-678001
 
< 0.1%
-45651.891
 
< 0.1%
-44762.11
 
< 0.1%
-44637.261
 
< 0.1%
-40678.561
 
< 0.1%
-37771.461
 
< 0.1%
-37682.324
< 0.1%
-36157.441
 
< 0.1%
-354751
 
< 0.1%
ValueCountFrequency (%)
376210.298
< 0.1%
360009.8512
< 0.1%
340918.424
 
< 0.1%
176334.651
 
< 0.1%
1301202
 
< 0.1%
1107001
 
< 0.1%
101708.521
 
< 0.1%
99222.641
 
< 0.1%
94593.871
 
< 0.1%
86819.811
 
< 0.1%

qt_consumo
Real number (ℝ)

High correlation  Skewed 

Distinct1316
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8967214
Minimum-3600
Maximum990000
Zeros14751
Zeros (%)0.3%
Negative278312
Negative (%)5.7%
Memory size37.2 MiB
2025-09-26T18:09:42.183568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3600
5-th percentile-1
Q11
median1
Q31
95-th percentile3
Maximum990000
Range993600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation449.43989
Coefficient of variation (CV)155.15468
Kurtosis4826682.9
Mean2.8967214
Median Absolute Deviation (MAD)0
Skewness2191.2389
Sum14128875
Variance201996.21
MonotonicityNot monotonic
2025-09-26T18:09:42.291939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13626739
74.4%
2571320
 
11.7%
-1210989
 
4.3%
3110801
 
2.3%
467237
 
1.4%
-247951
 
1.0%
547397
 
1.0%
616188
 
0.3%
014751
 
0.3%
0.0812989
 
0.3%
Other values (1306)151178
 
3.1%
ValueCountFrequency (%)
-36001
 
< 0.1%
-25001
 
< 0.1%
-20003
< 0.1%
-15001
 
< 0.1%
-14951
 
< 0.1%
-13001
 
< 0.1%
-1263.191
 
< 0.1%
-12501
 
< 0.1%
-1205.641
 
< 0.1%
-12002
< 0.1%
ValueCountFrequency (%)
9900001
 
< 0.1%
60002
 
< 0.1%
47001
 
< 0.1%
44991
 
< 0.1%
42981
 
< 0.1%
42501
 
< 0.1%
400011
< 0.1%
38001
 
< 0.1%
37002
 
< 0.1%
36005
< 0.1%

vl_movimento_2
Real number (ℝ)

High correlation  Skewed 

Distinct27585
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.434551
Minimum-71357.13
Maximum376210.29
Zeros8894
Zeros (%)0.2%
Negative278733
Negative (%)5.7%
Memory size37.2 MiB
2025-09-26T18:09:42.368997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-71357.13
5-th percentile-0.24
Q10.34
median1.06
Q33.93
95-th percentile23.04
Maximum376210.29
Range447567.42
Interquartile range (IQR)3.59

Descriptive statistics

Standard deviation967.73663
Coefficient of variation (CV)47.357861
Kurtosis98195.714
Mean20.434551
Median Absolute Deviation (MAD)0.86
Skewness271.8533
Sum99670340
Variance936514.18
MonotonicityNot monotonic
2025-09-26T18:09:42.466948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2236099
 
4.8%
0.21148809
 
3.1%
0.53125250
 
2.6%
0.28101664
 
2.1%
199080
 
2.0%
3.9390394
 
1.9%
0.580524
 
1.7%
0.3375667
 
1.6%
0.5272703
 
1.5%
0.8360578
 
1.2%
Other values (27575)3786772
77.6%
ValueCountFrequency (%)
-71357.131
 
< 0.1%
-678001
 
< 0.1%
-45651.891
 
< 0.1%
-44762.11
 
< 0.1%
-44637.261
 
< 0.1%
-40678.561
 
< 0.1%
-37771.461
 
< 0.1%
-37682.324
< 0.1%
-36157.441
 
< 0.1%
-354751
 
< 0.1%
ValueCountFrequency (%)
376210.298
< 0.1%
360009.8512
< 0.1%
340918.424
 
< 0.1%
176334.651
 
< 0.1%
1301202
 
< 0.1%
1107001
 
< 0.1%
101708.521
 
< 0.1%
99222.641
 
< 0.1%
94593.871
 
< 0.1%
86819.811
 
< 0.1%

id_item
Real number (ℝ)

High correlation 

Distinct3637
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4488245 × 109
Minimum691022
Maximum6.9102797 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.2 MiB
2025-09-26T18:09:42.566930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum691022
5-th percentile6910269
Q169102238
median6.9102424 × 108
Q36.9102521 × 109
95-th percentile6.9102739 × 109
Maximum6.9102797 × 109
Range6.9095887 × 109
Interquartile range (IQR)6.8411499 × 109

Descriptive statistics

Standard deviation3.3515037 × 109
Coefficient of variation (CV)0.97178145
Kurtosis-1.9829233
Mean3.4488245 × 109
Median Absolute Deviation (MAD)6.8411397 × 108
Skewness0.057288802
Sum1.6821779 × 1016
Variance1.1232577 × 1019
MonotonicityNot monotonic
2025-09-26T18:09:43.282936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6910251823540978
 
11.1%
6910269328797
 
6.7%
691022358286057
 
5.9%
6910268167287
 
3.4%
6910253709156174
 
3.2%
69102342153863
 
3.2%
6910290140266
 
2.9%
6910211325116066
 
2.4%
69102193107909
 
2.2%
691027386492689
 
1.9%
Other values (3627)2787454
57.1%
ValueCountFrequency (%)
69102260
 
< 0.1%
69102321258
 
< 0.1%
69102351569
 
< 0.1%
69102361298
 
< 0.1%
6910237181
 
< 0.1%
69102381503
 
< 0.1%
69102407492
 
0.2%
69102556775
 
0.1%
691026116
 
< 0.1%
6910268167287
3.4%
ValueCountFrequency (%)
69102796741
 
< 0.1%
69102796291
 
< 0.1%
69102796281
 
< 0.1%
69102795941
 
< 0.1%
69102795741
 
< 0.1%
6910279548156
< 0.1%
691027954724
 
< 0.1%
691027954666
< 0.1%
691027954563
< 0.1%
69102795342
 
< 0.1%

Interactions

2025-09-26T18:09:05.711201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:30.148458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:38.991773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:47.857126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:56.609014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:05.258350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:13.737577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:22.142131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:30.913116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:39.493478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:48.471482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:56.987807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:06.589817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:31.149963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:39.635537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:48.643632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:57.288191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:06.027930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:14.378863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:22.916693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:31.565277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:40.292615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:49.272516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:57.785184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:07.324358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:31.867631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:40.335131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:49.414903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:58.031372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:06.873856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:15.070580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:23.716883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:32.288165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:41.020220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:49.997406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:58.660369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:08.050349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:32.580408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:41.051038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:50.170357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:58.713523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:07.515235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:15.719640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:24.489734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:33.037667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:41.868092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:50.835114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:59.568186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:08.714567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:33.246376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:41.826419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:50.855731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:59.343909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:08.256233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:16.469808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:25.235441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:33.795829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-26T18:08:51.554957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:00.376346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:09.497438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:33.854586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:42.542472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:51.536439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:00.084598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:08.916372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:17.236319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:25.953125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:34.445813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:43.225284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:52.254250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:01.016352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:10.236110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:34.690897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:43.271079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:52.265389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:00.673270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:09.697741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:17.971637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:26.568538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:35.169746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:43.925276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:52.837772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:01.686954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:10.968313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-26T18:07:43.992625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:52.921781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:01.487478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:10.413993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:18.672277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:27.253371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:35.829021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:44.711832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:53.564367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:02.285463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:11.646024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:36.078118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:44.774858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:53.671184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:02.153092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:11.088701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:19.372749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:28.048519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:36.452997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:45.432693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:54.195439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:02.933527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:12.414397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:36.819507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:45.588878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:54.348968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:02.969185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:11.683360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:20.084856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:28.670341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:37.229815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:46.090820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:54.936784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:03.533168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:13.263042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:37.577981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:46.250204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:55.120161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:03.871136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:12.354790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:20.692867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:29.432722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:37.877580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:46.756933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:55.569415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:04.250583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:14.195631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:38.244386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:47.052107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:07:55.911302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:04.591472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:13.054594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:21.517207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:30.209445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:38.718221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:47.656562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:08:56.252925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-26T18:09:04.908554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-26T18:09:43.450758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
cd_acaocd_centro_custocd_local_estoquecd_materialcd_operacaods_estabelecimentods_operacaoid_itemnr_atendimentoqt_consumoqt_estoquevl_consumovl_estoquevl_movimentovl_movimento_2
cd_acao1.0000.0470.0580.0270.0490.0610.1300.0150.0240.0000.0000.0000.0000.0000.000
cd_centro_custo0.0471.0000.2030.010-0.0400.5950.2360.010-0.0080.0190.0350.0270.0420.0420.027
cd_local_estoque0.0580.2031.0000.033-0.1480.6240.2280.0330.0660.1370.1340.2930.3010.3010.293
cd_material0.0270.0100.0331.000-0.0980.0680.0651.0000.0030.0660.0590.1970.2070.2070.197
cd_operacao0.049-0.040-0.148-0.0981.0000.0051.000-0.0980.051-0.556-0.158-0.451-0.136-0.136-0.451
ds_estabelecimento0.0610.5950.6240.0680.0051.0000.2640.0610.0510.0000.0000.0000.0010.0010.000
ds_operacao0.1300.2360.2280.0651.0000.2641.0000.1070.0650.0020.0020.0120.0170.0170.012
id_item0.0150.0100.0331.000-0.0980.0610.1071.0000.0030.0660.0590.1970.2070.2070.197
nr_atendimento0.024-0.0080.0660.0030.0510.0510.0650.0031.000-0.0160.011-0.030-0.009-0.009-0.030
qt_consumo0.0000.0190.1370.066-0.5560.0000.0020.066-0.0161.0000.7630.4180.2280.2280.418
qt_estoque0.0000.0350.1340.059-0.1580.0000.0020.0590.0110.7631.0000.2680.2850.2850.268
vl_consumo0.0000.0270.2930.197-0.4510.0000.0120.197-0.0300.4180.2681.0000.8970.8971.000
vl_estoque0.0000.0420.3010.207-0.1360.0010.0170.207-0.0090.2280.2850.8971.0001.0000.897
vl_movimento0.0000.0420.3010.207-0.1360.0010.0170.207-0.0090.2280.2850.8971.0001.0000.897
vl_movimento_20.0000.0270.2930.197-0.4510.0000.0120.197-0.0300.4180.2681.0000.8970.8971.000

Missing values

2025-09-26T18:09:15.377787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-26T18:09:20.452890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-26T18:09:31.138483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ds_centro_custocd_centro_custods_estabelecimentocd_materialcd_acaonr_atendimentodt_movimento_estoquecd_local_estoqueds_local_estoquecd_conta_contabilnr_lote_contabilds_operacaocd_operacaodt_referenciaqt_estoquevl_estoquevl_movimentovl_consumoqt_consumovl_movimento_2id_item
0Posto Carambeí9722Laboratorio Unimed Ponta Grossa498621NaN2024-10-17 15:46:02.000537Posto Carambeí71111911113905.0NaNConsumo12024-10-01 00:00:00.0001.01.891.891.891.01.896910249862
1SADT - RADIOLOGIA9112Hospital Geral Unimed422811NaN2024-10-17 13:49:55.00011Almoxarifado71111911113905.0NaNConsumo12024-10-01 00:00:00.0003.064.3564.3564.353.064.356910242281
2Coleta Ambulatorial9765Laboratorio Unimed Ponta Grossa362151NaN2024-10-09 10:29:11.00062Almoxarifado Laboratório71111911113103.0NaNConsumo12024-10-01 00:00:00.000500.013.3513.3513.35500.013.356910236215
33º ANDAR - UNIDADE DE INTERNAÇÃO9103Hospital Geral Unimed692951NaN2024-10-17 14:04:47.00011Almoxarifado71111911113905.0NaNConsumo12024-10-01 00:00:00.00020.044.0044.0044.0020.044.006910269295
43º ANDAR - UNIDADE DE INTERNAÇÃO9103Hospital Geral Unimed168101NaN2024-10-17 14:04:47.00011Almoxarifado71111911113905.0NaNConsumo12024-10-01 00:00:00.0001.01.821.821.821.01.826910216810
53º ANDAR - UNIDADE DE INTERNAÇÃO9103Hospital Geral Unimed523061NaN2024-10-15 11:38:04.00057CAF71111911113103.0NaNConsumo12024-10-01 00:00:00.0006.063.4263.4263.426.063.426910252306
6MANUTENÇÃO9312Hospital Geral Unimed320321NaN2024-10-17 13:33:48.00011Almoxarifado71111911113904.0NaNConsumo12024-10-01 00:00:00.0001.05.035.035.031.05.036910232032
7UNIMED 24 HORAS9109Hospital Geral Unimed422811NaN2024-10-17 14:07:53.00011Almoxarifado71111911113905.0NaNConsumo12024-10-01 00:00:00.00015.0321.75321.75321.7515.0321.756910242281
8SADT - TOMOGRAFIA UNIMED9135Hospital Geral Unimed705981NaN2024-10-15 15:55:55.00057CAF71111911113103.0NaNConsumo12024-10-01 00:00:00.00010.0773.62773.62773.6210.0773.626910270598
9SADT - TOMOGRAFIA UNIMED9135Hospital Geral Unimed774221NaN2024-10-15 15:55:55.00057CAF71111911113103.0NaNConsumo12024-10-01 00:00:00.00010.01020.001020.001020.0010.01020.006910277422
ds_centro_custocd_centro_custods_estabelecimentocd_materialcd_acaonr_atendimentodt_movimento_estoquecd_local_estoqueds_local_estoquecd_conta_contabilnr_lote_contabilds_operacaocd_operacaodt_referenciaqt_estoquevl_estoquevl_movimentovl_consumoqt_consumovl_movimento_2id_item
4877530FARMÁCIA CENTRAL9306Hospital Geral Unimed10991NaN2023-04-10 05:58:43.00019Carrinho de emergência - 3° A Torre INaNNaNPerdas por estabilidade1372023-04-01 00:00:00.0001.0006.516.510.000.00.00691021099
4877531FARMÁCIA CENTRAL9306Hospital Geral Unimed684392NaN2023-02-02 15:18:19.0001Farmacia71111911113206.0NaNPerdas por estabilidade1372023-02-01 00:00:00.000-0.250-0.50-0.500.000.00.006910268439
4877532FARMÁCIA CENTRAL9306Hospital Geral Unimed540791NaN2023-02-07 12:17:03.0001Farmacia71111911113206.0NaNPerdas por estabilidade1372023-02-01 00:00:00.0000.4002.582.580.000.00.006910254079
4877533FARMÁCIA CENTRAL9306Hospital Geral Unimed8801NaN2023-01-26 10:49:11.0001Farmacia71111911113206.0NaNPerdas por estabilidade1372023-01-01 00:00:00.0000.0831.361.360.000.00.0069102880
4877534FARMÁCIA CENTRAL9306Hospital Geral Unimed8802NaN2023-01-26 10:49:11.0001Farmacia71111911113206.0NaNPerdas por estabilidade1372023-01-01 00:00:00.000-0.083-1.35-1.350.000.00.0069102880
4877535FARMÁCIA CENTRAL9306Hospital Geral Unimed512261NaN2023-12-12 14:24:43.00057CAF71111911113105.0NaNSaída por troca comercial1412023-12-01 00:00:00.0003.00040.5040.50-40.50-3.0-40.506910251226
4877536FARMÁCIA CENTRAL9306Hospital Geral Unimed554341NaN2023-12-12 14:19:59.00057CAF71111911113105.0NaNSaída por troca comercial1412023-12-01 00:00:00.0002.00034.4934.49-34.49-2.0-34.496910255434
4877537FARMÁCIA CENTRAL9306Hospital Geral Unimed30811NaN2023-11-24 16:32:22.00057CAF71111911113205.0NaNSaída por troca comercial1412023-11-01 00:00:00.00010.00026.0826.08-26.08-10.0-26.08691023081
4877538FARMÁCIA CENTRAL9306Hospital Geral Unimed712761NaN2023-11-24 16:53:57.00057CAF71111911113205.0NaNSaída por troca comercial1412023-11-01 00:00:00.0001.000558.48558.48-558.48-1.0-558.486910271276
4877539FARMÁCIA CENTRAL9306Hospital Geral Unimed547361NaN2023-11-24 16:42:44.00057CAF71111911113105.0NaNSaída por troca comercial1412023-11-01 00:00:00.0001.0002.002.00-2.00-1.0-2.006910254736

Duplicate rows

Most frequently occurring

ds_centro_custocd_centro_custods_estabelecimentocd_materialcd_acaonr_atendimentodt_movimento_estoquecd_local_estoqueds_local_estoqueds_operacaocd_operacaodt_referenciaqt_estoquevl_estoquevl_movimentovl_consumoqt_consumovl_movimento_2id_item# duplicates
03º ANDAR - UNIDADE DE INTERNAÇÃO9103Hospital Geral Unimed7603311992821.02023-04-20 08:09:48.0001FarmaciaDevolução Paciente52023-04-01 00:00:00.0000.0000.000.000.000.00.0069102760332
1CENTRO CIRÚRGICO9108Hospital Geral Unimed7530612156754.02023-10-08 16:22:08.000509OPME Centro CirúrgicoExecução Prescrição42023-10-01 00:00:00.0001.00070.0070.0070.001.070.0069102753062
2UNICON - UNIMED CENTRO DE ONCOLOGIA9110Hospital Geral Unimed12312144390.02023-10-06 11:00:00.00057CAFExecução Prescrição42023-10-01 00:00:00.0001.0001.321.321.321.01.32691021232
3UNICON - UNIMED CENTRO DE ONCOLOGIA9110Hospital Geral Unimed235812144390.02023-10-06 11:00:00.00057CAFExecução Prescrição42023-10-01 00:00:00.0003.0000.900.900.903.00.906910223582
4UNICON - UNIMED CENTRO DE ONCOLOGIA9110Hospital Geral Unimed5179012144390.02023-10-06 11:00:00.00057CAFExecução Prescrição42023-10-01 00:00:00.0001.0001.561.561.561.01.5669102517902
5UNICON - UNIMED CENTRO DE ONCOLOGIA9110Hospital Geral Unimed6008011930852.02023-01-24 10:00:25.00057CAFDevolução Paciente52023-01-01 00:00:00.0000.0005.005.00-5.00-0.1-5.0069102600802
6UNICON - UNIMED CENTRO DE ONCOLOGIA9110Hospital Geral Unimed6008011930852.02023-01-24 10:00:25.00057CAFDevolução Paciente52023-01-01 00:00:00.0000.0025.725.72-5.72-0.1-5.7269102600802
7UNICON - UNIMED CENTRO DE ONCOLOGIA9110Hospital Geral Unimed6008011930852.02023-01-24 10:00:25.00057CAFDevolução Paciente52023-01-01 00:00:00.0000.0035.465.46-5.46-0.1-5.4669102600802
8UNICON - UNIMED CENTRO DE ONCOLOGIA9110Hospital Geral Unimed6008011930852.02023-01-24 10:00:25.00057CAFDevolução Paciente52023-01-01 00:00:00.0000.0035.615.61-5.61-0.1-5.6169102600802
9UNICON - UNIMED CENTRO DE ONCOLOGIA9110Hospital Geral Unimed6008011930852.02023-01-24 10:00:25.00057CAFExecução Prescrição42023-01-01 00:00:00.0000.0005.005.005.000.15.0069102600802